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<h1>Independent Component Analysis</h1>

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ICA is a computational method for separating a multivariate (multi-channel)
signal into independent additive subcomponents. ICA separation of mixed signals
give very good results, providing two assumptions are met:<ul>
<li>the (unknown) source signals are independent of each other</li>
<li>the values in each source signal have non-gaussian distributions</li>
</ul>
Version implemented in this GUI is based on FastICA by Aapo Hyvärinen.
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<h2>Computing ICA</h2>
In the configuration dialog, you can choose<ul>
	<li>which channels are to be used for calculations, and</li>
	<li>should ICA be computed on a whole signal, or on a part of it.</li>
</ul>
After selecting “OK” computation starts and, in a while, a new (temporary) signal
will appear, consisting of ICA components calculated from the input signal.
The number of components will be the same as the number of selected channels.
Due to the randomization in FastICA implementation, the components will be
presented in random order.

<h2>Analysing components</h2>
After the computation is finished, you can use “Describe components” to preview
the spatial structure of the ICA components. After selecting the component from
the displayed list, its topography will be displayed: electrodes' color will
correspond to the associations between signal channels and ICA components.
Blue colour means the channel adds to the coefficient with the positive sign,
red colour denote the negative sign. Absolute value of the coefficient is represented
by intensity of the colour.

<h2>Zeroing components</h2>
By using “Zero selected components” method, it is possible to perform an inverse
transform, representing signal channels as a superposition of ICA components.
In addition, selected components may be suppressed in this process. In this way,
one can remove artifacts and/or noise, if they're represented as separate ICA
components.

<h2>Literature</h2>
<ol>
<li>Comon P.:
Independent component analysis, A new concept?,
<em>Signal Processing</em> 36 (1994), pp 287-314</li>
<li>Hyvärinen A.:
Fast and Robust Fixed-Point Algorithms for Independent Components Analysis,
<em>IEEE Transactions on Neural Networks</em> 10(3) 1999, pp 626-634</li>
</ol>

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